Connector: Enhancing the Traceability of Decentralized Bridge Applications via Automatic Cross-Chain Transaction Association

Decentralized bridge applications are important software that connects various blockchains and facilitates cross-chain asset transfer in the decentralized finance (DeFi) ecosystem which currently operates in a multi-chain environment. Cross-chain transaction association identifies and matches unique transactions executed by bridge DApps, which is important research to enhance the traceability of cross-chain bridge DApps. However, existing methods rely entirely on unobservable internal ledgers or APIs, violating the open and decentralized properties of blockchain. In this paper, we analyze the challenges of this issue and then present CONNECTOR, an automated cross-chain transaction association analysis method based on bridge smart contracts. Specifically, CONNECTOR first identifies deposit transactions by extracting distinctive and generic features from the transaction traces of bridge contracts.With the accurate deposit transactions, CONNECTOR mines the execution logs of bridge contracts to achieve withdrawal transaction matching. We conduct real-world experiments on different types of bridges to demonstrate the effectiveness of CONNECTOR. The experiment demonstrates that CONNECTOR successfully identifies 100% deposit transactions, associates 95.95% withdrawal transactions, and surpasses methods for CeFi bridges. Based on the association results, we obtain interesting findings about cross-chain transaction behaviors in DeFi bridges and analyze the tracing abilities of CONNECTOR to assist the DeFi bridge apps.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Information Forensics and Security
Published
2025-01-01
DOI
https://doi.org/10.1109/tifs.2025.3588249
Citations
6
Primary Topic
Business Process Modeling and Analysis
Type
article
Field-Weighted Citation Impact
13.70

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Connector: Enhancing the Traceability of Decentralized Bridge Applications via Automatic Cross-Chain Transaction Association

Zibin Zheng, Ziye Zheng, Jiajing Wu, Qinnan Zhang et al.
6 citations
IEEE Transactions on Information Forensics and Security
Business Process Modeling and Analysis
13.70
article

Connector: Enhancing the Traceability of Decentralized Bridge Applications via Automatic Cross-Chain Transaction Association

Zibin Zheng, Ziye Zheng, Jiajing Wu, Qinnan Zhang, Yuhong Nan, Dan Lin, Yuxin Su, Bowen Song
article en
6 citations

Abstract

Decentralized bridge applications are important software that connects various blockchains and facilitates cross-chain asset transfer in the decentralized finance (DeFi) ecosystem which currently operates in a multi-chain environment. Cross-chain transaction association identifies and matches unique transactions executed by bridge DApps, which is important research to enhance the traceability of cross-chain bridge DApps. However, existing methods rely entirely on unobservable internal ledgers or APIs, violating the open and decentralized properties of blockchain. In this paper, we analyze the challenges of this issue and then present CONNECTOR, an automated cross-chain transaction association analysis method based on bridge smart contracts. Specifically, CONNECTOR first identifies deposit transactions by extracting distinctive and generic features from the transaction traces of bridge contracts.With the accurate deposit transactions, CONNECTOR mines the execution logs of bridge contracts to achieve withdrawal transaction matching. We conduct real-world experiments on different types of bridges to demonstrate the effectiveness of CONNECTOR. The experiment demonstrates that CONNECTOR successfully identifies 100% deposit transactions, associates 95.95% withdrawal transactions, and surpasses methods for CeFi bridges. Based on the association results, we obtain interesting findings about cross-chain transaction behaviors in DeFi bridges and analyze the tracing abilities of CONNECTOR to assist the DeFi bridge apps.

IEEE Transactions on Information Forensics and SecurityVol. 20
Sun Yat-sen University (CN), Beijing Advanced Sciences and Innovation Center (CN), Beihang University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 1%
Business Process Modeling and Analysis
13.70
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.